BR Academy

Python for Finance and Crypto Data: pandas, APIs and Backtesting

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← Python for Finance and Crypto Data: pandas, APIs and Backtesting

1Python Essentials for Finance0/5

Set up Python, pip, Jupyter and VS Code for data work12m
NumPy arrays, broadcasting, and vectorized math for speed
Series and DataFrames: the core data structures for time series
Load CSV, JSON, Parquet; export results
Print debugging, assertions, and unit tests for data pipelines
Module quiz

2Pulling Data from APIs0/5

Making GET/POST requests; API keys and headers
Pull spot prices, OHLCV candles, and order book from Binance
CoinGecko, Messari, and other free crypto data sources
Query transactions, token holders, gas prices from Etherscan
Run SQL queries on Dune and fetch results via API
Module quiz

3Cleaning, Resampling and Merging Time Series0/5

Detect and fill NaN, forward-fill, interpolation
Remove duplicates, detect and handle outliers
Parse dates, convert timezones, align timestamps
Resample OHLCV to different frequencies (1m to 1h to 1d)
Join price, volume, and on-chain data by timestamp
Module quiz

4Indicators, Signals and Portfolio Analytics0/5

SMA, EMA, and trend detection
RSI, MACD, Stochastic oscillator
Bollinger Bands, ATR, and volatility measures
Calculate cumulative returns, max drawdown, Sharpe ratio
Win rate, profit factor, consecutive wins/losses
Module quiz

5Backtesting Strategies Properly0/5

Build a backtest loop; avoid look-ahead bias
Implement entry signals, stop-loss, take-profit
Kelly criterion, fixed fractional, risk per trade
Model realistic costs and fills
Grid search, walk-forward, out-of-sample testing
Module quiz

6Visualization and Reporting0/5

Line plots, candlestick charts, subplots
Heatmaps, distributions, correlation matrices
Plotly for interactive dashboards
Equity curve, drawdown chart, trade log, metrics summary
Save plots, generate HTML reports, version control
Module quiz

7Scheduling and Production0/5

Schedule Python scripts with cron and APScheduler
Log trades, errors, and performance metrics
Graceful failures, retry logic, alerting
Run on a VPS or cloud (AWS, Heroku, DigitalOcean)
Paper trading, order execution, risk controls
Module quiz

8Capstone Project u2014 Build and Deploy a Live Strategy0/3

Design a complete trading strategy from data to live execution
Write the full pipeline: data pull, signal, backtest, live
Deploy to a server, monitor performance, iterate
Module quiz

Cleaning, Resampling and Merging Time Series·Lesson 11 of 38·17 min

Detect and fill NaN, forward-fill, interpolation

This lesson is for enrolled learners

The concept in plain words Real market data has gaps: an exchange's API drops a candle during an outage, a weekend with no trading on a traditional asset leaves holes next to always-on crypto data, or a merge between two sources produces timestamps that only one source has values for. Pandas represents all of these gaps as NaN (Not a……

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